The Diverse Genomic Landscape of Clinically Low-risk Prostate Cancer.

BACKGROUND: Among men with clinically low-risk prostate cancer, we have previously documented heterogeneity in terms of clinical characteristics and genomic risk scores. OBJECTIVE: To further study the underlying tumor biology of this patient population, by interrogating broader patterns of gene expression among men with clinically low-risk tumors. DESIGN, SETTING, AND PARTICIPANTS: Prostate biopsies from 427 patients considered potentially suitable for active surveillance underwent central pathology review and genome-wide expression profiling. These cases were compared with 1290 higher-risk biopsy cases with diverse clinical features from a prospective genomic registry. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Average genomic risk (AGR) was determined from 18 published prognostic signatures, and MSigDB hallmark gene sets were analyzed using bootstrapped clustering methods. These sets were examined in relation to clinical variables and pathological and biochemical outcomes using multivariable regression analysis. RESULTS AND LIMITATIONS: A total of 408 (96%) biopsies passed RNA quality control. Based on AGR quartiles defined by the high-risk multicenter cases, the University of California, San Francisco (UCSF) low-risk patients were distributed across the quartiles as 219 (54%), 107 (26%), 61 (15%), and 21 (5%). Unsupervised clustering analysis of the hallmark gene set scores revealed three clusters, which were enriched for the previously described PAM50 luminal A, luminal B, and basal subtypes. AGR, but not the clusters, was associated with both pathological (odds ratio 1.34, 95% confidence interval [CI] 1.14-1.58) and biochemical outcomes (hazard ratio 1.53, 95% CI 1.19-1.93). These results may underestimate within-prostate genomic heterogeneity. CONCLUSIONS: Prostate cancers that are homogeneously low risk by traditional characteristics demonstrate substantial diversity at the level of genomic expression. Molecular substratification of low-risk prostate cancer will yield a better understanding of its divergent biology and, in the future may help personalize treatment recommendations. PATIENT SUMMARY: We studied the genomic characteristics of tumors from men diagnosed with low-risk prostate cancer. We found three main subtypes of prostate cancer with divergent tumor biology, similar to what has previously been found in women with breast cancer. In addition, we found that genomic risk scores were associated with worse pathology findings and prostate-specific antigen recurrence after surgery. These results suggest even greater genomic diversity among low-risk patients than has previously been documented with more limited signatures.

Development and Validation of a Novel Integrated Clinical-Genomic Risk Group Classification for Localized Prostate Cancer.

Purpose It is clinically challenging to integrate genomic-classifier results that report a numeric risk of recurrence into treatment recommendations for localized prostate cancer, which are founded in the framework of risk groups. We aimed to develop a novel clinical-genomic risk grouping system that can readily be incorporated into treatment guidelines for localized prostate cancer. Materials and Methods Two multicenter cohorts (n = 991) were used for training and validation of the clinical-genomic risk groups, and two additional cohorts (n = 5,937) were used for reclassification analyses. Competing risks analysis was used to estimate the risk of distant metastasis. Time-dependent c-indices were constructed to compare clinicopathologic risk models with the clinical-genomic risk groups. Results With a median follow-up of 8 years for patients in the training cohort, 10-year distant metastasis rates for National Comprehensive Cancer Network (NCCN) low, favorable-intermediate, unfavorable-intermediate, and high-risk were 7.3%, 9.2%, 38.0%, and 39.5%, respectively. In contrast, the three-tier clinical-genomic risk groups had 10-year distant metastasis rates of 3.5%, 29.4%, and 54.6%, for low-, intermediate-, and high-risk, respectively, which were consistent in the validation cohort (0%, 25.9%, and 55.2%, respectively). C-indices for the clinical-genomic risk grouping system (0.84; 95% CI, 0.61 to 0.93) were improved over NCCN (0.73; 95% CI, 0.60 to 0.86) and Cancer of the Prostate Risk Assessment (0.74; 95% CI, 0.65 to 0.84), and 30% of patients using NCCN low/intermediate/high would be reclassified by the new three-tier system and 67% of patients would be reclassified from NCCN six-tier (very-low- to very-high-risk) by the new six-tier system. Conclusion A commercially available genomic classifier in combination with standard clinicopathologic variables can generate a simple-to-use clinical-genomic risk grouping that more accurately identifies patients at low, intermediate, and high risk for metastasis and can be easily incorporated into current guidelines to better risk-stratify patients.

Ability of a Genomic Classifier to Predict Metastasis and Prostate Cancer-specific Mortality after Radiation or Surgery based on Needle Biopsy Specimens.

BACKGROUND: Decipher is a validated genomic classifier developed to determine the biological potential for metastasis after radical prostatectomy (RP). OBJECTIVE: To evaluate the ability of biopsy Decipher to predict metastasis and Prostate cancer-specific mortality (PCSM) in primarily intermediate- to high-risk patients treated with RP or radiation therapy (RT). DESIGN, SETTING, AND PARTICIPANTS: Two hundred and thirty-five patients treated with either RP (n=105) or RT±androgen deprivation therapy (n=130) with available genomic expression profiles generated from diagnostic biopsy specimens from seven tertiary referral centers. The highest-grade core was sampled and Decipher was calculated based on a locked random forest model. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Metastasis and PCSM were the primary and secondary outcomes of the study, respectively. Cox analysis and c-index were used to evaluate the performance of Decipher. RESULTS AND LIMITATIONS: With a median follow-up of 6 yr among censored patients, 34 patients developed metastases and 11 died of prostate cancer. On multivariable analysis, biopsy Decipher remained a significant predictor of metastasis (hazard ratio: 1.37 per 10% increase in score, 95% confidence interval [CI]: 1.06-1.78, p=0.018) after adjusting for clinical variables. For predicting metastasis 5-yr post-biopsy, Cancer of the Prostate Risk Assessment score had a c-index of 0.60 (95% CI: 0.50-0.69), while Cancer of the Prostate Risk Assessment plus biopsy Decipher had a c-index of 0.71 (95% CI: 0.60-0.82). National Comprehensive Cancer Network risk group had a c-index of 0.66 (95% CI: 0.53-0.77), while National Comprehensive Cancer Network plus biopsy Decipher had a c-index of 0.74 (95% CI: 0.66-0.82). Biopsy Decipher was a significant predictor of PCSM (hazard ratio: 1.57 per 10% increase in score, 95% CI: 1.03-2.48, p=0.037), with a 5-yr PCSM rate of 0%, 0%, and 9.4% for Decipher low, intermediate, and high, respectively. CONCLUSIONS: Biopsy Decipher predicted metastasis and PCSM from diagnostic biopsy specimens of primarily intermediate- and high-risk men treated with first-line RT or RP. PATIENT SUMMARY: Biopsy Decipher predicted metastasis and prostate cancer-specific mortality risk from diagnostic biopsy specimens.

Gene expression profiling-based prediction of response of colon carcinoma cells to 5-fluorouracil and camptothecin.

5-Fluorouracil (5-FU) is the most common chemotherapeutic agent used in the treatment of colorectal cancer, yet objective response rates are low. Recently, camptothecin (CPT) has emerged as an effective alternative therapy. Decisive means to determine treatment, based on the likelihood of response to each of these agents, could greatly enhance the management of this disease. Here, the ability of cDNA microarray-generated basal gene expression profiles to predict apoptotic response to 5-FU and CPT was determined in a panel of 30 colon carcinoma cell lines. Genes whose basal level of expression correlated significantly with 5-FU- and CPT-induced apoptosis were selected, and their predictive power was assessed using a "leave one out" jackknife cross-validation strategy. Selection of the 50 genes best correlated with 5-FU-induced apoptosis, but not 50 randomly selected genes, significantly predicted response to this agent. Importantly, this gene expression profiling approach predicted response more effectively than four previously established determinants of 5-FU response: thymidylate synthase and thymidine phosphorylase activity; and p53 and mismatch repair status. Furthermore, reanalysis of the database demonstrated that selection of the 149 genes best correlated with CPT-induced apoptosis maximally and significantly predicted response to this agent. These studies demonstrate that the basal gene expression profile of colon cancer cells can be used to predict and distinguish response to multiple chemotherapeutic agents and establish the potential of this methodology as a means by which rational decisions regarding choice of therapy can be approached.

Application of gene expression profiling to colon cell maturation, transformation and chemoprevention.

Methods for high-throughput analysis of profiles of gene expression that assay thousands of genes simultaneously are powerful approaches for understanding and classifying cell and tissue phenotype. This includes analysis of normal pathways of cell maturation and their perturbation in transformation, the sensitivity and mechanism of response of normal and tumor cells to physiological and pharmacological agents, and modulation of tumor risk and progression by nutritional factors. However, the complex data generated by such approaches raise difficulties in analysis. We will describe some of the methods we have used in analyzing databases generated in a number of projects in our laboratories. These include: the role of k-ras mutations in colon cell transformation; the role of p21(WAF1/cip1) in intestinal tumor formation and response to sulindac; the development of the absorptive and goblet cell lineages; sensitivity of colonic cells to chemotherapeutic agents; mechanisms that regulate c-myc expression utilizing novel methods of transcriptional imaging; and interaction of nutritional and genetic factors in modulation of intestinal tumor formation.

A gene expression profile that defines colon cell maturation in vitro.

Colonic epithelial cells undergo cell cycle arrest, lineage specific differentiation, and apoptosis, as they migrate along the crypt axis toward the lumenal surface. The Caco-2 colon carcinoma cell line models many of these phenotypic changes, in vitro. We used this model system and cDNA microarray analysis to characterize the genetic reprogramming that accompanies colon cell differentiation. The analyses revealed extensive yet functionally coordinated alterations in gene expression during the differentiation program. Consistent with cell differentiation reflecting a more specialized phenotype, the majority of changes (70%) were down-regulations of gene expression. Specifically, Caco-2 cell differentiation was accompanied by the coordinate down-regulation of genes involved in cell cycle progression and DNA synthesis, which reflected the concomitant reduction in cell proliferation. Simultaneously, genes involved in RNA splicing and transport, protein translation, folding, and degradation, were coordinately down-regulated, paralleled by a reduction in protein synthesis. Conversely, genes involved in xenobiotic and drug metabolism were up-regulated, which was linked to increased resistance of differentiated cells to chemotherapeutic agents. Increased expression of genes involved in extracellular matrix deposition, lipid transport, and lipid metabolism were also evident. Underlying these altered profiles of expression, components of signal transduction pathways, and several transcription factors were altered in expression.